Cross-sectional association between ZIP code-level gentrification and homelessness among a large community-based sample of people who inject drugs in 19 US cities. Issue 6 (20th June 2017)

• Record Type: Journal Article
• Authors: Linton, Sabriya L; Cooper, Hannah LF; Kelley, Mary E; Karnes, Conny C; Ross, Zev; Wolfe, Mary E; Friedman, Samuel R; Jarlais, Don Des; Semaan, Salaam; Tempalski, Barbara; Sionean, Catlainn; DiNenno, Elizabeth; Wejnert, Cyprian; Paz-Bailey, Gabriela
• Is Part Of: BMJ open. Volume 7:Issue 6(2017)
• Publication Date: 2017-06-20
• Subjects: PUBLIC HEALTH -- EPIDEMIOLOGY -- HIV & AIDS -- Substance misuse; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2016-013823
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Background: Housing instability has been associated with poor health outcomes among people who inject drugs (PWID). This study investigates the associations of local-level housing and economic conditions with homelessness among a large sample of PWID, which is an underexplored topic to date. Methods: PWID in this cross-sectional study were recruited from 19 large cities in the USA as part of National HIV Behavioral Surveillance. PWID provided self-reported information on demographics, behaviours and life events. Homelessness was defined as residing on the street, in a shelter, in a single room occupancy hotel, or in a car or temporarily residing with friends or relatives any time in the past year. Data on county-level rental housing unaffordability and demand for assisted housing units, and ZIP code-level gentrification (eg, index of percent increases in non-Hispanic white residents, household income, gross rent from 1990 to 2009) and economic deprivation were collected from the US Census Bureau and Department of Housing and Urban Development. Multilevel models evaluated the associations of local economic and housing characteristics with homelessness. Results: Sixty percent (5394/8992) of the participants reported homelessness in the past year. The multivariable model demonstrated that PWID living in ZIP codes with higher levels of gentrification had higher odds of homelessness in the past year (gentrification: adjusted OR=1.11, 95% CI=1.04 to 1.17). Conclusions: Additional research is needed to determine the mechanisms through which gentrification increases homelessness among PWID to develop appropriate community-level interventions.